Towards AI-Enabled Multimodal Diagnostics and Management of COVID-19 and Comorbidities in Resource-Limited Settings

Abstract

A conceptual artificial intelligence (AI)-enabled framework is presented in this study involving triangulation of various diagnostic methods for management of coronavirus disease 2019 (COVID-19) and its associated comorbidities in resource-limited settings (RLS). The proposed AI-enabled framework will afford capabilities to harness low-cost polymerase chain reaction (PCR)-based molecular diagnostics, radiological image-based assessments, and end-user provided information for the detection of COVID-19 cases and management of symptomatic patients. It will support self-data capture, clinical risk stratification, explanation-based intelligent recommendations for patient triage, disease diagnosis, patient treatment, contact tracing, and case management. This will enable communication with end-users in local languages through cheap and accessible means, such as WhatsApp/Telegram, social media, and SMS, with careful consideration of the need for personal data protection. The objective of the AI-enabled framework is to leverage multimodal diagnostics of COVID-19 and associated comorbidities in RLS for the diagnosis and management of COVID-19 cases and general support for pandemic recovery. We intend to test the feasibility of implementing the proposed framework through community engagement in sub-Saharan African (SSA) countries where many people are living with pre-existing comorbidities. A multimodal approach to disease diagnostics enabling access to point-of-care testing is required to reduce fragmentation of essential services across the continuum of COVID-19 care.

Prevalence of symptoms, comorbidities, fibrin amyloid microclots and platelet pathology in individuals with Long COVID/Post-Acute Sequelae of COVID-19 (PASC)

“…Unfortunately, treatment protocols are not yet widely available and current protocols are based on clinician-initiated approaches and experience in managing these patients. No clinical trials have been performed yet; however, this will be an important next step to urgently consider in parallel to patient monitoring using a multi-modal pathology-supported genetic testing approach [ 56 ]. For implementation of personalised medicine it will be essential to bring together clinicians, researchers, patients and policy makers to advance healthcare while allowing for adjustment and flexibility in view of new discoveries [ 57 ].…”

Older adults can use technology: why healthcare professionals must overcome ageism in digital health

“…Feedback from older adults is valuable for preventing poor design choices, refining digital health interventions, and increasing the likelihood of implementation into healthcare services. Research can increase representation by broadening eligibility criteria and modifying procedures that disproportionately exclude older adults (e.g., multiple comorbidities) [ 32 ]. This will generate more data on the potential to leverage digital health for multimodal assessments and treatments of comorbid conditions that become more common with aging.…”

Prevalence of symptoms, comorbidities, fibrin amyloid microclots and platelet pathology in individuals with Long COVID/ Post-Acute Sequelae of COVID-19 (PASC)

“…Unfortunately, treatment protocols are not yet widely available and current protocols are based on clinicianinitiated approaches and experience in managing these patients. No clinical trials have been performed yet; however, this will be an important next step to urgently consider in parallel to patient monitoring using a multimodal pathology-supported genetic testing approach [56]. For implementation of personalised medicine it will be essential to bring together clinicians, researchers, patients and policy makers to advance healthcare while allowing for adjustment and flexibility in view of new discoveries [57].…”